Multifurnace Optimization in Electric Smelting Plants by Load Scheduling and Control

For large electricity users, such as smelting plants, their electric loads cannot exceed a concerted limit in production. Traditional single-furnace optimization methods aim to satisfy the electric demand of a furnace to improve its production, and hence cannot consider the maximum demand constraint in a smelting plant. Maximum demand (MD) control is often utilized to keep the total electric demand within the limit via shedding the electric loads of some furnaces once the demand approaches the limit. However, the control method will enlarge the fluctuation of electric loads, which does harm to the production and causes a decline in energy-efficiency. In this paper, we propose a multifurnace optimization strategy to improve the production targets of a whole plant instead of a single furnace. In the strategy, an offline multiobjective load scheduling is first performed to assign electric loads for furnaces in each sampling period, taking into account of the MD constraint and production constraints. A multiobjective particle swarm optimization algorithm, combined with population initialization and constraint-handing strategies, is proposed to search for the Pareto optimal set of the scheduling problem, from which decision-makers can select one solution as the load scheduling program. A double closed-loop control mechanism is used to change the scheduled load into detailed load setpoints of furnaces and keep the actual loads up with the load setpoints. In the outer loop, the detailed load setpoints of furnaces are dynamically adjusted based on the deviation of actual loads from the scheduled loads. Thereafter, the desired setpoints are sent to the automatic control mechanism of each furnace, which is in the inner loop and responsible to keep the actual load up with the setpoint via a proportional-integral-derivative (PID) controller. The case study on a typical magnesia-smelting plant shows that the proposed multifurnace optimization strategy can achieve an increase of- about 12.29% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight reduction of 2.35% in electricity cost over the results of MD control. Note to Practitioners - For large electricity users, such as smelting plants, they are subjected to the maximum electric demand constraint. The maximum demand control device is widely adopted to solve the problem, but it will cause a decline in production output and energy-efficiency. This paper was motivated by improving the multiple production targets (i.e, the total production output, the product quality, and the total electricity cost) of a plant via load scheduling and control. In contrast to the maximum demand control, the load scheduling and control approach is a beforehand strategy that can optimize the operation. A case study on a magnesia-smelting plant shows that the proposed approach performs better than the maximum demand control technique.